1. Technical Field
This invention relates to a system for recognising anomalies contained within a set of data derived from an analogue waveform, particularly, though not exclusively, for locating noise in an audio signal. The invention may be applied to data from many different sources, for example, in the medical field to monitor signals from a cardiogram or encephalogram. It also has application in the field of monitoring machine performance, such as engine noise. A noise removal system is also described for use in combination with the present invention.
2. Related Art
Audio signals may be subject to two principal sources of noise: impulse noise and continuous noise.
There are a number of existing techniques for dealing with both sorts of noise. In particular, in order reduce the effects of continuous noise, such as a background “hum” in audio data, low-pass filters, dynamic filters, expanders and spectral subtraction are used. However, these techniques suffer from the disadvantage that the characteristic of the noise must be known at all times. The nature of noise makes it impossible to perfectly characterise it. Thus, in practice, even the most sophisticated filters remove genuine signal that is masked by the noise, as a result of the noise being imperfectly characterised. Using these techniques noise can only be removed with any degree of success from signals, such as speech signals, where the original signal is known.
Impulsive noise, such as clicks and crackles, is even more difficult to process because it cannot be characterised using dynamic, time resolved techniques. There are techniques for correcting the signal. However, problems remain in identifying the noise in the first place. Most impulsive noise removal techniques assume that the noise can be detected by simple measurements such as an amplitude threshold. However, noise is in general unpredictable and can never be identified in all cases by the measurement of a fixed set of features. It is extremely difficult to characterise noise, especially impulsive noise. If the noise is not fingerprinted accurately all attempts at spectral subtraction do not produce satisfactory results, due to unwanted effects. Even if the noise spectrum is described precisely, the results are dull due in part because the spectrum is only accurate at the moment of measurement.
Known impulse noise removal techniques include attenuation, sample and hold, linear interpolation and signal modeling. Signal modeling, as for example described in “Cedaraudio”, Chandra C, et al, “An efficient method for the removal of impulse noise from speech and audio signals”, Proc. IEEE International Symposium on Circuits and Systems, Monterey, Calif., Jun. 1998, pp 206-209, endeavours to replace the corrupted samples with new samples derived from analysis of adjacent signal regions. In this particular prior art technique, the correction of impulsive noise is attempted by constructing a model of the underlying resonant signal and replacing the noise by synthesised interpolation. However, notwithstanding the need to accurately detect the noise in the first place, this approach only works in those cases in which the model suits the desired signal and does not itself generate obtrusive artifacts.